DaRUS (University of Stuttgart)
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Replication Data for: Chiral Rh diene NHC Complexes and their Application in Rh-Catalyzed Cycloisomerization of Enynes
No full textIn this dataset, all simulation data are listed. The reaction mechanism for a cycloisomerization using an NHC Diene Rhodium catalyst was investigated using quantum chemical methods. All relevant intermediates and transition states were calculated for two different paths and two different NHCs, respectively. This includes geometry optimizations, energy calculations, and partial charge calculations.
The data for the reactant and products are given in separate folders, named appropriately. The other folders are named after the NHC that is part of the catalyst and subfolders are included that are named after the intermediate/transition state. The geometry optimization can be found in a directory "opt", the energy calculation in a directory "sp", and the partial charge in a folder "partial". All structures are named "*.xyz", and the input-files are named "*.chm", and the output-files are named "out". Data from collaborating groups can be found in a separate data set
Coupling MRI and XRCT to resolve structure–transport evolution during enzyme-induced calcite precipitation at the REV scale
No full textThis dataset supports the study of enzyme-induced calcite precipitation (EICP) in saturated sand columns under constant flow injection. The experiments investigate how CaCO3 precipitation alters pore structure, transport properties, and permeability at the representative elementary volume (REV) scale.
The dataset combines magnetic resonance imaging (MRI) and X-ray computed tomography (XRCT) with hydraulic measurements, enabling a direct linkage between pore-scale structural evolution and macroscopic transport behavio
Atmospheric transmission models for FIFI-LS: altitude 45kft, 39deg O3
No full textThis dataset contains atmospheric transmission models calculated by a modified version of ATRAN ("SDC ATRAN"). They are to be used as the default models for FIFI-LS data reduction with SOFIA Redux
The models are generated from a modified "SDC ATRAN" model based on Steve Lord's ATRAN. The most significant modification is a correction of 17O16O isotopologue abundance coefficients.
The models are stored in FITS binary table format with the columns "wavelength" and "transmission". All models of the same altitude (and same wavelength range) have the same number of wavelength elements.
File naming convention:
atran_sdc_xxK_yydeg_zzpwv_39deg_2nlayer_40-300mum_bt.fits
where xx is the flight altitude in kft, yy is the zenith angle in degree, and zz the
precipitable water vapor value in micron.
This dataset contains models calculated for a 2 layer atmospheric model with an ozone profile identified by "39deg".</p
Supplementary Material for "Efficient Prediction of Multicomponent Adsorption Isotherms and Enthalpies of Adsorption in MOFs Using Classical Density Functional Theory"
No full textThe data that support the findings of the article "Efficient Prediction of Multicomponent Adsorption Isotherms and Enthalpies of Adsorption in MOFs using Classical Density Functional Theory". The dataset includes all adsorption data obtained from molecular simulations and from classical DFT calculations ("data"), as well as the force field parameters ("force_fields") and PC-SAFT parameter ("pc_saft_parameters") used in this work and the input files for RASPA ("raspa_files").
We recommend viewing the data by choosing the option "Tree"
Data for Confinement-Induced Z-Selectivity in the Rhodium N-Heterocyclic Carbene-Catalyzed Hydroboration of Terminal Alkynes
No full textThe concept of solid catalyst coated with a 1 nm layer of an ionic liquid [BMIM+ BF4-] was employed, resulting in a 22-fold increase in β(Z)-selectivity for Rh(I)- and Rh(III)- complexes based on N- and O-chelating N-heterocyclic carbenes (NHCs).
The primary data files for the synthesized ligand and complexes as well as performed catalysis are provided. The labeling of the data files proceeded according to the corresponding figures in the supporting information. It includes NMR of the synthesized complexes and catalysis, physisorption measurements and IR measurements. The Crystal data for the complexes Rh3, Rh5 and Rh6 is deposited in the Cambridge Structural Database (CSD) of the Cambridge Crystallographic Data Centre (CCDC)
Opossum
No full textOpossum
Opossum is an optimization plug-in for Grasshopper (for Rhino) that implements model-based and evolutionary optimization algorithms for single- and multi-objective problems. The plugin integrates GUI components into Grasshopper to configure runs, visualize results, and persist optimizer state inside Grasshopper documents.
This repository contains a .NET/Grasshopper plugin project targeting .NET Framework 4.8.
Highlights
Model-based single-objective optimization (RBFOpt)
Evolutionary single-objective optimizer (CMA-ES)
Multi-objective RBFMOpt optimizer
Multiple multi-objective algorithms via pygmo
Interactive Grasshopper window and result serialization
Notable folders and classes
Opossum2_0_Proto_A/ — main plugin project. Contains component classes, UI windows, serialization utilities, and resources.
OptComponent.cs — main Grasshopper component implementation: UI hooks, serialization/deserialization, and component registration.
Resources/ — embedded icons, images, and other resource files used by the plugin.
Solver/ — optimization solver interface and implementations (RBFOpt, pygmo, etc.).
Requirements and dependencies
Development / build
Microsoft Visual Studio (2017, 2019, or 2022) with .NET Framework 4.8 targeting pack installed.
The Grasshopper and Rhino SDK (or runtime assemblies) to resolve references to Grasshopper.dll, GH_IO.dll, and RhinoCommon.dll.
Runtime
Rhino with a matching version of Grasshopper (the plugin runs inside Grasshopper).
Opossum installed via Food4Rhino or PackageManager to obtain Python backend and dependencies.
The plugin expects to be loaded inside Grasshopper; it does not run as a standalone .NET application.
Build instructions
Open the solution in Visual Studio.
Ensure project references to Rhino/Grasshopper assemblies are valid (point to the installed Rhino/Grasshopper runtime or SDK). These references are typically not committed and must be resolved on your machine.
Restore NuGet packages.
Build the Opossum2_0_Proto_A project in Debug or Release configuration.
The produced assembly will generally be a .dll file or .gha (Grasshopper plugin), depending on how the project is configured.
Install and run
Install Opossum from Food4Rhino or PackageManager.
Copy the built .gha/.dll to Grasshopper's Components folder.
Restart Rhino/Grasshopper.
The plugin registers its component(s) under the category set in the component registration (for example Params -> Util).
Usage notes
OptComponent manages component serialization (stores GUIDs of linked variables, simulators, and objectives) so saved Grasshopper files keep references and results across sessions.
Results can be serialized into the Grasshopper document and later reloaded. The component distinguishes whether results were produced by the optimizer or loaded from a file.
If your installation uses an external Python optimizer, verify that the Python environment is accessible from the host machine and any interop code is configured to find the Python interpreter and libraries.
Development notes
To debug the plugin, run Rhino and attach the Visual Studio debugger to the Rhino process.
When changing serialization formats (chunk names, GUIDs, or data layout), be careful to preserve compatibility with existing saved Grasshopper documents or provide migration logic.
UI classes (windows) are created at runtime and registered with Grasshopper's FormShepard; ensure proper disposal to avoid resource leaks.
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Atmospheric transmission models for FIFI-LS: altitude 38kft, 39deg O3
No full textThis dataset contains atmospheric transmission models calculated by a modified version of ATRAN ("SDC ATRAN"). They are to be used as the default models for FIFI-LS data reduction with SOFIA Redux
The models are generated from a modified "SDC ATRAN" model based on Steve Lord's ATRAN. The most significant modification is a correction of 17O16O isotopologue abundance coefficients.
The models are stored in FITS binary table format with the columns "wavelength" and "transmission". All models of the same altitude (and same wavelength range) have the same number of wavelength elements.
File naming convention:
atran_sdc_xxK_yydeg_zzpwv_39deg_2nlayer_40-300mum_bt.fits
where xx is the flight altitude in kft, yy is the zenith angle in degree, and zz the
precipitable water vapor value in micron.
This dataset contains models calculated for a 2 layer atmospheric model with an ozone profile identified by "39deg".</p
Supplemental data for "Robust inverse material design with physical guarantees using the Voigt-Reuss Net"
No full textThis repository contains supplemental data for the article
"Robust inverse material design with physical guarantees using the Voigt-Reuss Net",
accepted for publication in the International Journal for Numerical Methods in Engineering by Sanath Keshav and Felix Fritzen.
The data in this DaRUS repository complements the accompanying open-source implementation of the Voigt-Reuss net and enables
reproducibility of the numerical results in the manuscript [1]. The datasets are generated by solving periodic
small-strain linear elasticity homogenization problems for a large number of biphasic periodic RVEs.
Effective stiffness tensors were computed with our open-source implementation of the
Fourier-Accelerated Nodal Solvers (FANS) [4] on voxelized microstructures under periodic boundary conditions.
The repository contains two labeled datasets:
(i) 3D linear elasticity.
We use an open 3D microstructure dataset (90,000 stochastic microstructures, resolution 192x192x192),
together with 236 scalar, image-derived morphological descriptors per microstructure [2].
For each microstructure, we sample three non-dimensional parameters that encode the bulk and shear moduli of the two phases, and compute the corresponding effective stiffness
tensor (symmetric positive definite, 6x6 in Mandel notation).
Overall, the dataset contains ~1.18 million microstructure-material combinations and includes the
train/validation/test splits used in the paper.
(ii) 2D plane-strain elasticity.
We use periodic microstructures obtained from thresholded trigonometric fields parameterized by an amplitude matrix A and a threshold [3].
For each sample, we provide the generator parameters, the rendered microstructure image,
and the homogenized plane-strain stiffness tensor (symmetric positive definite, 3x3 in Voigt notation). The same split definitions and metadata used for training and evaluation are included to reproduce the forward-prediction comparisons and inverse-design experiments.
Further details on file formats, naming conventions, and the exact contents of each file are provided in README.md.
[1] Keshav, S., and Fritzen, F. (2026). Robust inverse material design with physical guarantees using the Voigt-Reuss Net, International Journal for Numerical Methods in Engineering. https://doi.org/10.1002/nme.70296
[2] Prifling, B., Röding, M., Townsend, P., Neumann, M., and Schmidt, V. (2020). Large-scale statistical learning for mass transport prediction in porous materials using 90,000 artificially generated microstructures [dataset]. Zenodo. https://doi.org/10.5281/zenodo.4047774
[3] Boddapati, J., & Daraio, C. (2024). Planar structured materials with extreme elastic anisotropy. Materials & Design, 246, 113348.
https://doi.org/10.1016/j.matdes.2024.113348
[4] Leuschner, M., and Fritzen, F. (2018). Fourier-Accelerated Nodal Solvers (FANS) for homogenization problems. Computational Mechanics, 62(3), 359-392.
https://doi.org/10.1007/s00466-017-1501-5 <br
Replication Data for: Taylor-Green Vortex Benchmark Suite
No full text
Source code, results and numerical cases in OpenFOAM's format for the Taylor-Green Vortex benchmark suite presented in T. Zirwes, M. Sontheimer, F. Zhang, A. Abdelsamie, F.E. Hernandez Perez, O.T. Stein, H.G. Im, A. Kronenburg, H. Bockhorn, "Assessment of numerical accuracy and parallel performance of OpenFOAM and its reacting flow extension EBIdnsFoam", Flow, Turbulence and Combustion, volume 111, pages 567-602, 2023, https://doi.org/10.1007/s10494-023-00449-8
All simulations performed with OpenFOAM v1712 with the following cases:
Step 1: 2D incompressible Taylor-Green vortex: Full numerical setup with all required input files to re-run the simulation with two discretization schemes: cubic interpolation and WENO4. Additionally, a sampled line of the velocity and vorticity field is included.
Step 2: 3D incompressible Taylor-Green vortex: Similar to Step 1, but for the 3D case. Additionally includes source-code for a modified pimpleFoam solver that limits the time step with the Fourier number.
Step 3: 3D multi-species Taylor-Green vortex: Numerical setup for a 3D Taylor-Green vortex flow with prescribed species and temperature profiles. Setup and results for both detailed diffusion and unity Lewis number diffusion models.
Step 4: 3D reacting Taylor-Green vortex: Similar to step 3, but chemical reactions for hydrogen combustion are activated.
The authors gratefully acknowledge the computing time provided on the high-performance computer HoreKa by the National High-Performance Computing Center at KIT (NHR@KIT). This center is jointly supported by the Federal Ministry of Education and Research and the Ministry of Science, Research and the Arts of Baden-Württemberg, as part of the National High-Performance Computing (NHR) joint funding program (https://www.nhr-verein.de/en/our-partners). HoreKa is partly funded by the German Research Foundation (DFG)
Data for: "Engine-Airframe Integration - From Froude Theorem to Numerical Flow Simulation"
No full textThis dataset contains the data of the intake maps for the underwing intake and the bli intake. The data corresponds to figure 9 of the manuscript. The intake maps are determined with the simulation software OpenFOAM. The results are given for the individual Mach numbers as the total pressure ratio over the non-dimensional mass flow parameter. The results are an addition to the result of the conference paper "Engine-Airframe Integration - From Froude Theorem to Numerical Flow Simulation" presented at EASN 2025. (2025-11-30